Pipeline interchange/transmix

ABSTRACT

In one embodiment, a process is taught where the process begins by flowing a first product through a first pipeline and flowing a second product through a second pipeline. The process then produces a blended product by mixing both the first product and the second product within a pipeline interchange which is connected downstream to both the first pipeline and the second pipeline. The blended product then flows from the pipeline interchange to a third pipeline that is connected downstream of pipeline interchange. The blended product is analyzed in the third pipeline with an automated analyzer that is capable of physical and/or chemically analyzing the blended product in the third pipeline and generating blended data. The blended data is then interpreted in a data analyzer by comparing the physical and/or chemical characteristics of the blended data to an optimal blended data and determining the adjustments in the flow of the first product and the flow of the second product to achieve optimal blended data from the blended product. The adjustments are then communicated to adjust the flow of the first product in the first pipeline and the flow of the second product in the second pipeline.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation-in-part application which claims thebenefit of and priority to U.S. patent application Ser. No. 16/391,817filed Apr. 23, 2019 entitled “Pipeline Interchange” andContinuation-in-Part patent application Ser. No. 16/557,315 filed Aug.30, 2019 entitled “Pipeline Interchange/Transmix,” both of which arehereby incorporated by reference in their entirety.

STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH OR DEVELOPMENT

None.

FIELD OF THE INVENTION

This invention relates to a pipeline interchange/transmix.

BACKGROUND OF THE INVENTION

Pipelines often transport physically and chemically different types ofpetroleum product in the same pipeline. In some pipeline systems thedifferent types of petroleum products get blended together to achieve anoptimal final petroleum product. For example, different pipelines mightbring in different types of crude oil to be processed in a refinery anda refinery might have to manage Canadian crude, shale crude, and sweetcrude all within the same day.

This situation adds extreme complexity to refineries as they must manageall of the different types of crude to make a standardized refinedpetroleum product. Unfortunately, due to all the different physical andchemical characteristics that are each unique to each of the crudeproducts it is difficult to manage all of these differentcharacteristics to product a standardized refined petroleum product.Different physical and chemical characteristics may require the refineryto handle the crude differently prior to being processed into a refinedpetroleum product.

In other types of pipeline systems, a pipeline operator sends differentproducts in “batches” down a central pipeline that must be separated.For example, an operator might send gasoline for several hours, and thenswitch to jet fuels, before switching to diesel fuel. The process oftracking the customer's batch or product through the pipeline is donethrough analyzing the different products within a pipeline.

In situations where pipeline operators send different products through acentral pipeline, the product is measured at the receipt point in thepipeline and again upon delivery to document the amount of product movedfrom point A to point B. Many pipeline systems require pipeline ownersto meet defined common product specifications for each product shipped.This requires pipeline owners to regularly analyze many differentproperties of products in a refinery or a terminal. In these scenarios,a sample of product is analyzed either before entering the pipeline orduring to give an analytical result. Once the area in which the sampleis taken from reaches a splitter, the operation of the splitter isadjusted based on the properties of the refined product.

Current analytical techniques, in a pipeline, require that the sample ofproduct are taken with a hydrometer and adhere to guidelines such asASTM guidelines. To adhere to these guidelines pipeline operators musttake the sample by either stopping the flow of a pipeline or taking asample from a flowing pipeline. Stopping a pipeline is expensive and notideal. Taking a sample from a flowing pipeline can mean large quantitiesof the refined product can flow through the pipeline prior to theanalytical results being generated.

A pipeline interchange or transmix is generally known in the industry asa location where products that flow through a pipeline are eitherseparated or combined. In the petroleum industry, this pipelineinterchange generally consists of substantially horizontal pipes thatoperate within either a pipeline terminal, a refinery, a marine dock, ora rail terminal. Typically, one pipeline will be tasked withtransporting various petroleum products and a method of separating therefined petroleum products within the pipeline to different pipelines orstorage compartments is required. Alternatively, two or more pipelineswill be tasked with transporting various petroleum products and a methodof combining the petroleum products into one central pipeline isrequired.

There exists a need for a configuration that would allow a pipelineoperator to obtain near instantaneous analytical results from samples ofproduct and relay that information to either separate or combinepetroleum products.

BRIEF SUMMARY OF THE DISCLOSURE

In one embodiment, a process is taught where the process begins byflowing a first product through a first pipeline and flowing a secondproduct through a second pipeline. The process then produces a blendedproduct by mixing both the first product and the second product within apipeline interchange which is connected downstream to both the firstpipeline and the second pipeline. The blended product then flows fromthe pipeline interchange to a third pipeline that is connecteddownstream of pipeline interchange. The blended product is analyzed inthe third pipeline with an automated analyzer that is capable ofphysical and/or chemically analyzing the blended product in the thirdpipeline and generating blended data. The blended data is theninterpreted in a data analyzer by comparing the physical and/or chemicalcharacteristics of the blended data to an optimal blended data anddetermining the adjustments in the flow of the first product and theflow of the second product to achieve optimal blended data from theblended product. The adjustments are then communicated to adjust theflow of the first product in the first pipeline and the flow of thesecond product in the second pipeline.

In another embodiment, a process is taught where the process begins byflowing a first product through a first pipeline and flowing a secondproduct through a second pipeline. In this embodiment, the first productin the first pipeline is optionally analyzed with a first productautomated analyzer that is capable of physical and/or chemicallyanalyzing the first product in the first pipeline and generating a firstproduct data. Additionally, in this embodiment, the second product inthe second pipeline is optionally analyzed with a second productautomated analyzer that is capable of physical and/or chemicallyanalyzing the second product in the second pipeline and generating asecond product data. The process then produces a blended product bymixing both the first product and the second product within a pipelineinterchange which is connected downstream to both the first pipeline andthe second pipeline. The blended product then flows from the pipelineinterchange to a third pipeline that is connected downstream of pipelineinterchange. The blended product is analyzed in the third pipeline withan automated analyzer that is capable of physical and/or chemicallyanalyzing the blended product in the third pipeline and generatingblended data. The first product data, second product data, and blendeddata is then interpreted in a data analyzer by comparing the physicaland/or chemical characteristics of the blended data to an optimalblended data, the physical and/or chemical characteristics of the firstdata to an optimal first data, and the physical and/or chemicalcharacteristics of the second data to an optimal second data. The dataanalyzer then determines the adjustments in the flow of the firstproduct and the flow of the second product to achieve optimal blendeddata from the blended product. The adjustments are then communicated toadjust the flow of the first product in the first pipeline and the flowof the second product in the second pipeline

BRIEF DESCRIPTION OF THE DRAWINGS

A more complete understanding of the present invention and benefitsthereof may be acquired by referring to the follow description taken inconjunction with the accompanying drawings in which:

FIG. 1 depicts one embodiment of the pipeline interchange.

FIG. 2 depicts one embodiment of the pipeline interchange.

FIG. 3 depicts one embodiment of the process.

FIG. 4 depicts one embodiment of the pipeline interchange.

FIG. 5 depicts one embodiment of the pipeline interchange.

FIG. 6 depicts one embodiment of the pipeline interchange.

FIG. 7 depicts one embodiment of the pipeline interchange.

FIG. 8 depicts one embodiment of the process.

FIG. 9 depicts one embodiment of the process.

FIG. 10 depicts a typical NMR spectrum for a sample comprising crude oil(lower panel), and a view of the same spectrum with the Y-axis amplified(upper panel).

FIG. 11 depicts a diagram demonstrating the decomposition of spectraldata according to wavelet theory.

FIG. 12 depicts progressive decomposition of NIR spectral data toproduce approximation (a) and detail (d) coefficients data from thefirst to sixth levels.

FIG. 13 depicts a plot of samples representing two classes separated bya separating hyperplane.

FIG. 14 is a simplified diagram depicting an embodiment of the presentinventive process and system.

FIG. 15 is a simplified diagram depicting an embodiment of the presentinventive process and system.

FIG. 16 depicts a simplified diagram of a hot liquid process simulatorapparatus.

FIG. 17 is a plot of wavelet coefficient data for each of two principlecomponents in crude oil samples that were recognized by patternrecognition genetic algorithm.

FIG. 18 is a plot representing correlation between the measured foulingthermal resistance (x-axis) for a given training sample versus thefouling thermal resistance predicted by SVM (y-axis).

DETAILED DESCRIPTION

Turning now to the detailed description of the preferred arrangement orarrangements of the present invention, it should be understood that theinventive features and concepts may be manifested in other arrangementsand that the scope of the invention is not limited to the embodimentsdescribed or illustrated. The scope of the invention is intended only tobe limited by the scope of the claims that follow.

In one embodiment, a pipeline interchange flows a product through anupstream pipeline. An automated analyzer is connected to the upstreampipeline, wherein the automated analyzer analyzes a sample of theproduct, and wherein the analyzer is capable of analyzing differentphysical and/or chemical characteristics of the product and generating adata sample. An automatic splitter is then placed downstream of theautomated slipstream analyzer. In this embodiment, the automaticsplitter is capable of receiving and interpreting the data sample fromthe automated analyzer and directing the product into at least threedifferent downstream pipelines, wherein at least one of the downstreampipelines is a transmix pipeline and wherein at least one of thedownstream pipelines returns the product upstream of the automatedanalyzer.

In yet another embodiment, a pipeline interchange flows a refinedpetroleum product through an upstream pipeline. An automated analyzercomprising an inlet, a return and an analyzer, wherein the automatedanalyzer is used to collect a sample, analyze the sample of the refinedpetroleum product, and return the sample of the refined petroleumproduct flowing through the upstream pipeline, and wherein the analyzeris capable of analyzing different physical and/or chemicalcharacteristics of the refined petroleum product. An automatic splitteris then placed downstream of the automated slipstream analyzer andgenerating a data sample. In this embodiment, the automatic splitter iscapable of receiving and interpreting the data sample from the automatedanalyzer and directing the refined petroleum product into at least threedifferent downstream pipelines, wherein at least one of the downstreampipelines is a transmix pipeline and wherein at least one of thedownstream pipelines returns the refined petroleum product upstream ofthe automated analyzer.

In one embodiment, a pipeline interchange flows a product through anupstream pipeline. An automated analyzer is connected to the upstreampipeline to analyze different physical and/or chemically properties inthe product and generate data from the product without extracting asample from the upstream pipeline. An automatic splitter is placeddownstream of the automated analyzer, capable of receiving andinterpreting the data from the automated analyzer and directing therefined petroleum product into at least three different downstreampipelines, wherein at least one of the downstream pipelines is atransmix pipeline.

In yet another embodiment, a pipeline interchange flows a refinedpetroleum product through an upstream pipeline. An automated analyzerconnected to the upstream pipeline wherein the automated analyzerphysically and/or chemically analyzes the refined petroleum product andgenerates data from the refined petroleum product without extracting asample from the upstream pipeline. An automatic splitter is placeddownstream of the automated analyzer, capable of receiving andinterpreting the data from the automated analyzer and directing therefined petroleum product into at least three different downstreampipelines, wherein at least one of the downstream pipelines is atransmix pipeline.

In one embodiment, the method begins by flowing a product stream throughan upstream pipeline comprising a first product stream. The productstream is then continuously analyzed with an automated analyze toproduce data. The first product stream downstream is then directeddownstream of the automated analyzer to a downstream first productstream pipeline. The method then changes the product stream flowingthrough the upstream pipeline from the first product stream to a secondproduct stream without purging the first product stream from theupstream pipeline, thereby creating a transmix product stream within theupstream pipeline wherein the transmix product stream comprises amixture of the first product stream and the second product stream. Thedata from the automated analyzer is then analyzed with an automaticsplitter, wherein the product stream flowing through the upstreampipeline no longer matches the physical and/or chemical characteristicsof the first product stream. The automatic splitter then directs thetransmix product stream downstream of the automatic splitter to adownstream transmix pipeline. As the data from the automated analyzer isstill analyzed by the automatic splitter the product stream flowingthrough the upstream pipeline matches the physical and/or chemicalcharacteristics of the second product stream. The automatic splitterthen directs the second product stream downstream of the automaticsplitter to a downstream second product stream pipeline.

In yet another embodiment, the method begins by flowing a product streamthrough an upstream pipeline comprising a first refined petroleumproduct stream. The product stream is then continuously analyzed with anautomated analyze to produce data. The first refined petroleum productstream downstream is then directed downstream of the automated analyzerto a downstream first refined petroleum product stream pipeline. Themethod then changes the product stream flowing through the upstreampipeline from the first refined petroleum product stream to a secondrefined petroleum product stream without purging the first refinedpetroleum product stream from the upstream pipeline, thereby creating atransmix product stream within the upstream pipeline wherein thetransmix product stream comprises a mixture of the first refinedpetroleum product stream and the second refined petroleum productstream. The data from the automated analyzer is then analyzed with anautomatic splitter, wherein the product stream flowing through theupstream pipeline no longer matches the physical and/or chemicalcharacteristics of the first refined petroleum product stream. Theautomatic splitter then directs the transmix product stream downstreamof the automatic splitter to a downstream transmix pipeline. As the datafrom the automated analyzer is still analyzed by the automatic splitterthe product stream flowing through the upstream pipeline matches thephysical and/or chemical characteristics of the second refined petroleumproduct stream. The automatic splitter then directs the second refinedpetroleum product stream downstream of the automatic splitter to adownstream second refined petroleum product stream pipeline.

In one embodiment, a pipeline interchange is described where a firstproduct flows through a first pipeline and a second product flowsthrough a second pipeline. A pipeline interchange is connecteddownstream to both the first pipeline and the second pipeline, whereinthe pipeline interchange blends the first product flowing through thefirst pipeline with the second product flowing through the secondpipeline. A third pipeline is connected downstream to the pipelineinterchange, wherein the third pipeline flows a blended product createdfrom the blending of the first product and the second product in thepipeline interchange. An automated analyzer can be situated downstreamof the pipeline interchange capable of physical and/or chemicallyanalyzing the blended product and generating blended data. A dataanalyzer is also positioned to interpret the blended data andcommunicate adjustments to the flow of both the first product and thesecond product to achieve desired physical and/or chemicalcharacteristics in the blended product.

In another embodiment, a pipeline interchange is described where a firstproduct flows through a first pipeline and a second product flowsthrough a second pipeline. In this embodiment, an optional first productautomated analyzer is situated near the first pipeline to physicaland/or chemically analyze the first product and generate first productdata. Additionally, in this embodiment, an optional second productautomated analyzer is situated near the second pipeline to physicaland/or chemically analyze the second product and generate second productdata. A pipeline interchange is connected downstream to both the firstpipeline and the second pipeline, wherein the pipeline interchangeblends the first product flowing through the first pipeline with thesecond product flowing through the second pipeline. A third pipeline isconnected downstream to the pipeline interchange, wherein the thirdpipeline flows a blended product created from the blending of the firstproduct and the second product in the pipeline interchange. An automatedanalyzer can be situated downstream of the pipeline interchange capableof physical and/or chemically analyzing the blended product andgenerating blended data. A data analyzer is also positioned to interpretthe blended data; the first product data; and the second product dataand communicate adjustments to the flow of both the first product andthe second product to achieve desired physical and/or chemicalcharacteristics in the blended product.

In one embodiment, a pipeline interchange is described where a firstproduct flows through a first pipeline and a second product flowsthrough a second pipeline. In this embodiment, a first product automatedanalyzer is situated near the first pipeline to physical and/orchemically analyze the first product and generate first product data.Additionally, in this embodiment, a second product automated analyzer issituated near the second pipeline to physical and/or chemically analyzethe second product and generate second product data. A pipelineinterchange is connected downstream to both the first pipeline and thesecond pipeline, wherein the pipeline interchange blends the firstproduct flowing through the first pipeline with the second productflowing through the second pipeline. A third pipeline is connecteddownstream to the pipeline interchange, wherein the third pipeline flowsa blended product created from the blending of the first product and thesecond product in the pipeline interchange. A data analyzer is alsopositioned to interpret the first product data and the second productdata and communicate adjustments to the flow of both the first productand the second product to achieve desired physical and/or chemicalcharacteristics in the blended product.

In another embodiment, a pipeline interchange is described where a firstproduct flows through a first pipeline and a second product flowsthrough a second pipeline. In this embodiment, a first product automatedanalyzer is situated near the first pipeline to physical and/orchemically analyze the first product and generate first product data.Additionally, in this embodiment, a second product automated analyzer issituated near the second pipeline to physical and/or chemically analyzethe second product and generate second product data. A pipelineinterchange is connected downstream to both the first pipeline and thesecond pipeline, wherein the pipeline interchange blends the firstproduct flowing through the first pipeline with the second productflowing through the second pipeline. A third pipeline is connecteddownstream to the pipeline interchange, wherein the third pipeline flowsa blended product created from the blending of the first product and thesecond product in the pipeline interchange. An optional automatedanalyzer can be situated downstream of the pipeline interchange capableof physical and/or chemically analyzing the blended product andgenerating blended data. A data analyzer is also positioned to interpretthe blended data; the first product data; and the second product dataand communicate adjustments to the flow of both the first product andthe second product to achieve desired physical and/or chemicalcharacteristics in the blended product.

In one embodiment, a process is taught where the process begins byflowing a first product through a first pipeline and flowing a secondproduct through a second pipeline. The process then produces a blendedproduct by mixing both the first product and the second product within apipeline interchange which is connected downstream to both the firstpipeline and the second pipeline. The blended product then flows fromthe pipeline interchange to a third pipeline that is connecteddownstream of pipeline interchange. The blended product is analyzed inthe third pipeline with an automated analyzer that is capable ofphysical and/or chemically analyzing the blended product in the thirdpipeline and generating blended data. The blended data is theninterpreted in a data analyzer by comparing the physical and/or chemicalcharacteristics of the blended data to an optimal blended data anddetermining the adjustments in the flow of the first product and theflow of the second product to achieve optimal blended data from theblended product. The adjustments are then communicated to adjust theflow of the first product in the first pipeline and the flow of thesecond product in the second pipeline.

In another embodiment, a process is taught where the process begins byflowing a first product through a first pipeline and flowing a secondproduct through a second pipeline. In this embodiment, the first productin the first pipeline is optionally analyzed with a first productautomated analyzer that is capable of physical and/or chemicallyanalyzing the first product in the first pipeline and generating a firstproduct data. Additionally, in this embodiment, the second product inthe second pipeline is optionally analyzed with a second productautomated analyzer that is capable of physical and/or chemicallyanalyzing the second product in the second pipeline and generating asecond product data. The process then produces a blended product bymixing both the first product and the second product within a pipelineinterchange which is connected downstream to both the first pipeline andthe second pipeline. The blended product then flows from the pipelineinterchange to a third pipeline that is connected downstream of pipelineinterchange. The blended product is analyzed in the third pipeline withan automated analyzer that is capable of physical and/or chemicallyanalyzing the blended product in the third pipeline and generatingblended data. The first product data, second product data, and blendeddata is then interpreted in a data analyzer by comparing the physicaland/or chemical characteristics of the blended data to an optimalblended data, the physical and/or chemical characteristics of the firstdata to an optimal first data, and the physical and/or chemicalcharacteristics of the second data to an optimal second data. The dataanalyzer then determines the adjustments in the flow of the firstproduct and the flow of the second product to achieve optimal blendeddata from the blended product. The adjustments are then communicated toadjust the flow of the first product in the first pipeline and the flowof the second product in the second pipeline

In one embodiment, a process is taught where the process begins byflowing a first product through a first pipeline and flowing a secondproduct through a second pipeline. In this embodiment, the first productin the first pipeline is analyzed with a first product automatedanalyzer that is capable of physical and/or chemically analyzing thefirst product in the first pipeline and generating a first product data.Additionally, in this embodiment, the second product in the secondpipeline is analyzed with a second product automated analyzer that iscapable of physical and/or chemically analyzing the second product inthe second pipeline and generating a second product data. The processthen produces a blended product by mixing both the first product and thesecond product within a pipeline interchange which is connecteddownstream to both the first pipeline and the second pipeline. Theblended product then flows from the pipeline interchange to a thirdpipeline that is connected downstream of pipeline interchange. The firstproduct data and the second product data is then interpreted in a dataanalyzer by comparing the physical and/or chemical characteristics ofthe physical and/or chemical characteristics of the first data to anoptimal first data and the physical and/or chemical characteristics ofthe second data to an optimal second data. The data analyzer thendetermines the adjustments in the flow of the first product and the flowof the second product to achieve optimal blended data from the blendedproduct. The adjustments are then communicated to adjust the flow of thefirst product in the first pipeline and the flow of the second productin the second pipeline.

In another embodiment, a process is taught where the process begins byflowing a first product through a first pipeline and flowing a secondproduct through a second pipeline. In this embodiment, the first productin the first pipeline is analyzed with a first product automatedanalyzer that is capable of physical and/or chemically analyzing thefirst product in the first pipeline and generating a first product data.Additionally, in this embodiment, the second product in the secondpipeline is analyzed with a second product automated analyzer that iscapable of physical and/or chemically analyzing the second product inthe second pipeline and generating a second product data. The processthen produces a blended product by mixing both the first product and thesecond product within a pipeline interchange which is connecteddownstream to both the first pipeline and the second pipeline. Theblended product then flows from the pipeline interchange to a thirdpipeline that is connected downstream of pipeline interchange. Theblended product is then optionally analyzed in the third pipeline withan automated analyzer that is capable of physical and/or chemicallyanalyzing the blended product in the third pipeline and generatingblended data. The first product data, second product data, and blendeddata is then interpreted in a data analyzer by comparing the physicaland/or chemical characteristics of the blended data to an optimalblended data, the physical and/or chemical characteristics of the firstdata to an optimal first data, and the physical and/or chemicalcharacteristics of the second data to an optimal second data. The dataanalyzer then determines the adjustments in the flow of the firstproduct and the flow of the second product to achieve optimal blendeddata from the blended product. The adjustments are then communicated toadjust the flow of the first product in the first pipeline and the flowof the second product in the second pipeline.

In one embodiment, an optional pipeline interchange automated analyzercan be placed within the pipeline interchange itself to measure thephysical or chemical characteristics that flows through the pipelineinterchange. In this embodiment, the data analyzer analyzes the datagenerated from the automated pipeline interchange automated analyzer.

In one embodiment, the flow of the first product in the first pipeline,the flow of the second product in the second pipeline or even the flowof the first product and the second product within the pipelineinterchange can be controlled by valves. In some embodiments, the valvescan be closed, open, or partially open to adjust the ratio of firstproduct and second product in the blended product.

In some embodiments, the ratio of first product to second product in theblended product can be 100% first product to 0% second product, 0% firstproduct to 100% second product. In other embodiments, the ratio can beanywhere from 0.1% to 99.9% first product to 0.1% to 99.9% secondproduct.

In one embodiment, a programmable logic controller can be used toprovide the control signals from the first product automated analyzer tothe data analyzer, the second product automated analyzer to the dataanalyzer, and the automated analyzer to the data analyzer. Additionally,a programmable logic controller can be used to provide to the signalfrom the data analyzer to the first pipeline, second pipeline, and thepipeline interchange to adjust the flow of the first product or thesecond product to produce a blended product.

In one embodiment, a system is taught of separating transmix, gasoline,and diesel fuel from a refined petroleum product within a pipeline. Inthis embodiment, the transmix, gasoline, diesel fuel and refinedpetroleum product has optical properties which the system utilizes toassist in the distinguishing the features to separate the refinedpetroleum product. The system begins by having an automated slipstreamanalyzer in fluid connection with the refined petroleum product withinthe pipeline. A programmable logic controller is then used for governingthe flow of refined petroleum product through the pipeline. A processoris then programmed to receive the refined petroleum product, estimatethe flash temperature and provide a control signal to the programmablelogic controller according to the maximum preprogrammed limits. In thisembodiment, the programmable logic controller is configured to adjustthe pipeline to provide an indication regarding when to direct the flowof the refined petroleum product into at least three downstreampipelines.

In yet another embodiment, a system is taught of blending transmix anddiesel fuel from a refined petroleum product within a pipeline. In thisembodiment, the transmix, gasoline fuel, diesel fuel, and refinedpetroleum product has optical properties which the system utilizes toassist in the distinguishing the features to blend the transmix anddiesel fuel. The system begins by having a transmix pipeline and adiesel fuel pipeline in fluid connection downstream of a refined productpipeline. In this embodiment, an automated slipstream analyzer is influid connection with the refined petroleum product. A program ablelogic controller is used for governing the flow of refined petroleumproduct towards the transmix pipeline, the diesel fuel pipeline, or thegasoline pipeline. Additionally, a processor is programmed to receivethe refined petroleum product, calculate the amount of flash temperatureof the refined petroleum product and provide a control signal to theprogrammable logic controller according to the preprogrammed limit forthe blended diesel fuel. An automatic splitter, downstream of theautomated slipstream analyzer, configured to adjust the flow oftransmix, from either or both the transmix pipeline or the transmixreservoir, into the blended diesel fuel pipeline according to thepreprogrammed limit for diesel fuel.

In another embodiment, a system is taught of blending transmix andgasoline fuel from a refined petroleum product within a pipeline. Inthis embodiment, the transmix, gasoline fuel, diesel fuel, and refinedpetroleum product has optical properties which the system utilizes toassist in the distinguishing the features to blend the transmix andgasoline fuel. The system begins by having a transmix pipeline, a dieselfuel pipeline, a gasoline pipeline, and a transmix reservoir in fluidconnection downstream of a refined product pipeline, wherein thetransmix pipeline flows into the transmix reservoir. In this embodiment,an automated slipstream analyzer in fluid connection with the refinedpetroleum product. A programmable logic controller is used for governingthe flow of refined petroleum product towards the transmix pipeline, thediesel fuel pipeline, or the gasoline pipeline. Additionally, aprocessor is programmed to receive the refined petroleum product,calculate the amount of flash temperature of the refined petroleumproduct and provide a control signal to the programmable logiccontroller according to the preprogrammed limit for gasoline fuel. Anautomatic splitter, downstream of the automated slipstream analyzer,configured to adjust the flow of transmix, from either or both thetransmix pipeline or the transmix reservoir, into the blended gasolinefuel pipeline according to the preprogrammed limit for gasoline fuel.

In yet another embodiment, a system is taught for blending transmix anddiesel fuel from a refined petroleum product within a pipeline. In thisembodiment, the transmix, gasoline fuel, diesel fuel, and refinedpetroleum product has optical properties which the system utilizes toassist in the distinguishing the features to blend the transmix anddiesel fuel. The system begins by having a transmix pipeline, a dieselfuel pipeline, a gasoline pipeline, and a transmix reservoir in fluidconnection downstream of a refined product pipeline, wherein thetransmix pipeline flows into the transmix reservoir. In this embodiment,an automated slipstream analyzer is in fluid connection with the refinedpetroleum product. Additionally, a programmable logic controller forgoverning the flow of refined petroleum product towards the transmixpipeline, the diesel fuel pipeline, or the gasoline pipeline. Aprocessor programmed to receive the refined petroleum product, calculatethe amount of flash temperature of the refined petroleum product andprovide a control signal to the programmable logic controller accordingto the preprogrammed limit for diesel fuel. An automatic splitter,downstream of the automated slipstream analyzer is configured to adjustthe flow of transmix, from either or both the transmix pipeline or thetransmix reservoir, into the diesel fuel pipeline according to themaximum preprogrammed limit for diesel fuel.

In another embodiment, a system is taught for blending transmix andgasoline fuel from a refined petroleum product within a pipeline. Inthis embodiment, the transmix, gasoline fuel, diesel fuel, and refinedpetroleum product has optical properties which the system utilizes toassist in the distinguishing the features to blend the transmix andgasoline fuel. The system begins by having a transmix pipeline, a dieselfuel pipeline, a gasoline pipeline, and a transmix reservoir in fluidconnection downstream of a refined product pipeline, wherein thetransmix pipeline flows into the transmix reservoir. In this embodiment,an automated slipstream analyzer is in fluid connection with the refinedpetroleum product. Additionally, a programmable logic controller forgoverning the flow of refined petroleum product towards the transmixpipeline, the diesel fuel pipeline, or the gasoline pipeline. Aprocessor programmed to receive the refined petroleum product, calculatethe amount of flash temperature of the refined petroleum product andprovide a control signal to the programmable logic controller accordingto the preprogrammed limit for gasoline fuel. An automatic splitter,downstream of the automated slipstream analyzer is configured to adjustthe flow of transmix, from either or both the transmix pipeline or thetransmix reservoir, into the diesel fuel pipeline according to themaximum preprogrammed limit for gasoline fuel.

In one non-limiting embodiment, the methodology of measuring the flashtemperature can be done via ASTM test method D86 or ASTM test methodD93. In other embodiments, different optical test methods can be used tomeasure the flash temperature. In one embodiment, when using ASTM testmethod D86 to measure gasoline fuel the final boiling point can bearound 437° F. In another embodiment, when using ASTM test method D93 tomeasure ultra low sulfur fuel or kerosene, the flash temperature isgreater than 100.4° F. In yet another embodiment, when using ASTM testmethod D93 to measure ultra low sulfur diesel, the flash temperature isgreater than 125.6° F. In an alternate embodiment the arrangementdescribes a pipeline interchange, wherein the pipeline interchange has arefined petroleum product flowing through an upstream pipeline. Thepipeline interchange can also have an automated slipstream analyzerconnected to the upstream pipeline comprising an inlet, a return and ananalyzer. In this embodiment, the automated slipstream analyzer is usedto collect a sample, analyze the sample, generate data from the sampleand return the sample of the refined petroleum product flowing In onenon-limiting embodiment, the methodology of measuring the flashtemperature can be done via ASTM test method D86 or ASTM test methodD93. In other embodiments, different optical test methods can be used tomeasure the flash temperature. In one embodiment, when using ASTM testmethod D86 to measure gasoline fuel the final boiling point can bearound 437° F. In another embodiment, when using ASTM test method D93 tomeasure ultra low sulfur fuel or kerosene, the flash temperature isgreater than 100.4° F. In yet another embodiment, when using ASTM testmethod D93 to measure ultra low sulfur diesel, the flash temperature isgreater than 125.6° F. In an alternate embodiment the arrangementdescribes a pipeline interchange, wherein the pipeline interchange has arefined petroleum product flowing through an upstream pipeline. Thepipeline interchange can also have an automated slipstream analyzerconnected to the upstream pipeline comprising an inlet, a return and ananalyzer. In this embodiment, the automated slipstream analyzer is usedto collect a sample, analyze the sample, generate data from the sampleand return the sample of the refined petroleum product flowing.

In some embodiment, an upstream pipeline is generally defined as thepipeline upstream of the pipeline interchange and a downstream pipelineis generally defined as the pipeline downstream of the pipelineinterchange.

In an alternate embodiment the different automated analyzers can alsohave an automated slipstream analyzer connected to the pipelinecomprising an inlet, a return and an analyzer. In this embodiment, theautomated slipstream analyzer is used to collect a sample, analyze thesample, generate data from the sample and return the sample of theproducts flowing through the different pipelines.

One embodiment describes a pipeline interchange, wherein the pipelineinterchange has a refined petroleum product or crude petroleum productflowing through the pipeline. The pipeline interchange can also have anautomated slipstream analyzer connected to the upstream pipelinecomprising an inlet, a return and an analyzer. In this embodiment, theautomated slipstream analyzer is used to collect a sample, analyze thesample, generate data from the sample and return the sample of therefined petroleum product flowing through the upstream pipeline.

When the pipeline interchange is used as a splitter, the pipelineinterchange can have an automatic splitter, downstream of the automatedslipstream analyzer, capable of receiving and interpreting the data fromthe automated slipstream analyzer and directing of directing the refinedpetroleum product into at least three different downstream pipelines,wherein at least one of the downstream pipelines is an intermixpipeline.

In this embodiment, an upstream pipeline is generally defined as thepipeline upstream of the pipeline interchange and a downstream pipelineis generally defined as the pipeline downstream of the pipelineinterchange.

When the pipeline interchange is used as a blender, the pipelineinterchange can have both a first pipeline and a second pipeline, bothupstream pipelines, placed upstream of the pipeline interchange. Thethird pipeline, or downstream pipeline, can be placed downstream of thepipeline interchange. The pipeline interchange in this embodiment iscapable of blending the products that flow in the first pipeline and thesecond pipeline together to generate a blended product in the thirdpipeline.

In one embodiment, the pipeline interchange is an integral part of apipeline terminal or a refinery. A pipeline interchange is generallythought of as a place where different pipelines can either intersect ordiverge. The size of the upstream pipeline and downstream pipelines canvary based upon the products they are transporting. In one embodiment,the upstream pipeline and the downstream pipeline can range from about 4inches in diameter to about 48 inches in diameter. These pipelines caneither flow downstream of the pipeline interchange into other pipelines,into storage containers or storage tanks, into marine vessels, or intorail cars. These medians can also include an intermix. In oneembodiment, at least three different downstream pipelines can beconnected to pipeline storage tanks or intermix storage tanks.

In other embodiments, there can be two different upstream or downstreampipelines, three different upstream or downstream pipelines, fourdifferent upstream or downstream pipelines, five different upstream ordownstream pipelines, six different upstream or downstream pipelines ormore. The number of different upstream or downstream pipelines willdepend upon the different types of products flowing into the pipelineinterchange. In other embodiments, the one of the downstream pipelinescan be dedicated for contaminates. In yet another embodiment, thedownstream and upstream pipelines can be interchangeable for their uses.

In other embodiments, there can be two different downstream pipelines,three different downstream pipelines, four different downstreampipelines, five different downstream pipelines, six different downstreampipelines or more. The number of different downstream pipelines willdepend upon the different types of refined petroleum products flowingthrough the upstream pipeline. In other embodiments, the one of thedownstream pipelines can be dedicated for contaminates. In yet anotherembodiment, the downstream pipelines can be interchangeable for theiruses.

The refined product that flows through the pipelines can be any liquidor gaseous product that can be derived from crude oils through processessuch as catalytic cracking and fractional distillation. These productscan have physical and chemical characteristics that differ according tothe type of crude oil and subsequent refining processes. Different typesof product streams can be flowed through the pipelines includinggasoline, diesel fuels, jet fuels, naphtha, marine gas oils, liquefiedpetroleum gasses, kerosene, lubricating oils and different types of fueloils such as No. 2, No. 4, No. 5, and No. 6. In some embodiments, twodifferent products can flow through the pipeline to have a first productstream and a second product stream, or a first refined product streamand a second refined product stream. In one embodiment, the firstproduct stream and the second product stream is chemically distinctivefrom each other.

It is envisioned, in one embodiment, that the flow of the refinedproduct would not be decreased when flowing through the pipelineinterchange.

In one embodiment of the invention the analyzer is an optical analyzer.Unlike hydrometers that manually measure the density of the refinedpetroleum product it is envisioned that the pipeline interchange willutilize a continuous or periodic optical analyzer. In one embodiment,the pipeline interchange operates without a hydrometer. In anon-limiting embodiment, types of analyzers that can be used include,near-infrared analyzers, mid-infrared analyzers, far-infrared analyzer,Raman analyzer, acoustic analyzer, UV-visible analyzer, densityanalyzer, NMR analyzer, viscometer analyzer, terahertz spectroscopyanalyzer, conductivity analyzer, pH analyzer, mass spectrometryanalyzer, turbidity analyzer, particle size analyzer, electron lossspectroscopy analyzer, fluorescence analyzer, micro- andnano-electromechanical systems analyzers, chromatography analyzer,electrophoresis analyzer, microwave resonance analyzer, cavity-ringdownanalyzer, cavity-enhanced analyzer, dielectric spectroscopy, surfaceplasmon resonance analyzer, quartz crystal microbalance analyzer,biosensing analyzers, time resolved spectrometers, and micro totalanalytical systems analyzer. The quantitative data generated by theseanalyzers can include data for premium gasoline, jet fuel, diesel fueland unleaded gasoline.

In one embodiment, the analyzers can be used to analyze contaminants inthe refined product. These contaminants can be compounds such as:benzene, toluene, ethylbenzene, xylenes, methyl tertiary butyl ethers,sulfur, vanadium, iron, zinc, lead scavengers, moisture, organic-basedadditives, or even biomaterial. In other embodiments, the opticalanalyzer can be used to analyze optical properties of refined productsor refined petroleum products such as: flash temperature octane numbers,research octane numbers, motor octane numbers, antiknock index, boilingpoint, density, viscosity, molecular type compositions, elementalanalysis, freezing point, carbon residue, pour point, cloud point, vaporpressure, reid vapor pressure, flammability range, wax and asphaltenecontents, cetane number, aniline point, and carbon-to-hydrogen ratios.

By utilizing analyzers, the automatic splitter and/or data analyzer willbe able to receive rapid and reliable data regarding the composition ofthe refined petroleum product that is flowing through the pipeline.Additionally, the samples taken and returned to the pipeline by theoptical analyzers allow the petroleum product to be reused instead ofconventional hand measurement methods that can modify the petroleumproduct and therefore make it unsuitable of being returned to thepipeline or being used as a conventional fuel. Depending on the physicalor chemical method used cannot take samples.

In alternate embodiments it is possible to place multiple analyzersalong a pipeline, either the upstream pipelines, downstream pipelines,first pipeline, second pipeline, or third pipeline. The multipleanalyzers will provide data to the automatic splitter and/or dataanalyzer to analyzer the data. It is theorized by placing multipleanalyzers the automatic splitter and/or data analyzer would haveadditional data to generate instructions and adjustments to the pipelineinterchange system thereby producing more accurate results.

In one embodiment, automatic splitter comprises both a physical devicecapable of directing the product to the different pipelines, such asdownstream pipelines, and a data analyzer capable of analyzing the data.In some embodiments, the data analyzer is physically located at adifferent location than the automatic splitter.

It is theorized that by using a continuous or periodic analyzer that thedata generated can be received and interpreted by the automatic splitterand/or data analyzer is faster than conventional methods. The automaticsplitter and/or data analyzer can then be able to determine the precisemoment the refined petroleum product changes from one type of petroleumproduct to an intermix/transmix and from the intermix/transmix toanother type of petroleum product. Intermix/transmix is defined as arandom mixture of on-specification fuels that due to their mixing nolonger meet a specific fuel specification, such intermix/transmix fluidscan be directed to an intermix/transmix pipeline, which can be connectedto an intermix/transmix storage tank, which will be redistributed backto a refinery to generate petroleum products that meet productspecification requirements.

In one example the analyzer is an infrared analyzer such as midinfrared,near-infrared analyzer, far infrared. These types of analyzers aretypically spectroscopic using technical designs such as dispersive,fourier-transform, microelectromechanical systems, acousto-optic tunablefilters, and super-luminescent LED's. The analytical information thatthese types of analyzers usually obtain are molecular vibrations fromabsorbance/transmittance of wavenumbers. Additionally, these types ofanalyzers can be obtained from samples withdrawn from the pipeline, orfrom probes with direct contact with the product in the pipeline.

In another example the analyzer is a Raman analyzer. These types ofanalyzers are typically spectroscopic using technical designs such asdispersive, interferometric, fourier-transform, czerny-turner, andprocess micro spectroscopy. The analytical information that these typesof analyzers usually obtain are molecular scattering of wavenumbers orintensity. Additionally, these types of analyzers can be obtained fromsamples withdrawn from the pipeline, or from probes with direct contactwith the product in the pipeline.

In another example the analyzer is an acoustic analyzer. These types ofanalyzers are typically sensors that use acoustics such as active,passive, transverse, and longitudinal. The analytical information thatthese types of analyzers usually obtain are acoustic emissions andchanges in velocity and/or amplitude of mechanical waves such asvibration, ultrasonic, infrasonic, subsonic, and supersonic. Thesephases or frequencies can be obtained from probes with direct contactwith the product in the pipeline or by external clamp-on to the pipelineusing straps, glue, screw, magnet, tape, etc. such that the incidentwave penetrates through the pipe wall.

In another example the analyzer is a UV-visible analyzer. These types ofanalyzers are typically spectroscopic that measure electronictransitions of absorbance or transmittance of wavelengths. Additionally,these types of analyzers can be obtained from samples withdrawn from thepipeline.

In another example the analyzer is a density meter. These types ofanalyzers are typically gravimetric using technical designs such asvibrating elements. The analytical information that these types ofanalyzers typically obtain are density information from vibrationfrequency which is generally inversely proportional to density.Additionally, these types of analyzers can be obtained from sampleswithdrawn from the pipeline, or from probes with direct contact with theproduct in the pipeline.

In another example the analyzer is an NMR analyzer. These types ofanalyzers are typically spectroscopic using technical designs such astime-domain or high resolution. The analytical information that thesetypes of analyzers typically obtain are absolute signal intensities,free induction decays, and chemical shifts. These intensity/chemicalshift signals can be obtained from probes with direct contact with theproduct, using devices that withdraw samples from the pipeline to theinstrument or using a configuration where samples are collected andanalyzed on the instrument which is placed in close proximity to thepipeline.

In another example the analyzer is a viscometer. These types ofanalyzers are typically viscometric using technical designs such asvibrating elements. The analytical information that these types ofanalyzers typically obtain include the natural frequency of vibrationchanges that occur with the fluid viscosity. The bandwidth of thevibrating elements can be converted to a viscosity measurement usingcalibration coefficients. These frequencies can typically be obtainedfrom probes with direct contact with the product in the pipeline.

In another example the analyzer is a terahertz spectroscopy analyzer.These types of analyzers typically utilize spectroscopic analysis tomeasure the rotational and vibrational transitions or molecularvibrational or torsional modes in condensed phase materials. The signalsfrom this type of analyzer are typically absorbance or photocurrentintensity and can be obtained from either sample withdrawn from thepipeline, or from probes with direct contact with the product in thepipeline

In other embodiments, the data obtains from the analyzers can beinterpreted by using chemometrics. Chemometrics would assist the methodby modelling and correlating underlying relationships, extracting andenhancing subtle but informative chemical features, recognizing signalpatterns and fingerprints, and predicting new properties usinganalytical signals that are generated from the analyzers within themethod. In one embodiment, the automatic splitter can analyze the datausing chemometrics to determine the modelling relationship of theproduct or refined petroleum product to the data. This modellingrelationship can be continuously updated as additional data is providedby the automated analyzer. Having the automatic splitter to analyze thedata chemometrically would allow the automatic splitter to more clearlydefine the chemical characteristics that distinguish a first productstream to a second product stream, or from a first refined productstream to a second refined product stream.

In other embodiments, it is envisioned that automatic splitter can thenbe able to determine the precise moment the refined petroleum productchanges from one type of petroleum product to one that containscontaminates. The automatic splitter then would direct the contaminatedrefined product to a pipeline that can be redistributed back to arefinery instead of to storage tanks for consumer use.

The automatic splitter can be from 1 meter to 500 meters downstream ofthe automated slipstream analyzer. In one embodiment, the automaticsplitter can be up to 1 kilometer, 2 kilometers or even 5 kilometersdownstream of the automated slipstream analyzer. The automatic splittercan be any splitter capable of directing the flow of the upstreampipeline into the different downstream pipelines. This can consist of avalve on each of the downstream pipelines or a central splitter used todirect the flow of fluid into one or more of the downstream pipelines.

In one embodiment, the automated slipstream analyzer is located inlineof the upstream pipeline. As shown in FIG. 1, a side profile of anupstream pipeline 2 is shown with an automated slipstream analyzer 4deposed within. The automated slipstream analyzer has an inlet 6 capableof collecting a sample and a return 8 capable of returning the sample ofrefined petroleum product flowing through the upstream pipeline. Theautomated slipstream analyzer can analyze the sample collected from theinlet and generate data from the sample. The data generated from theautomated slipstream analyzer can be transferred wirelessly 10 or by awired connection 12 to an automatic splitter 14 located downstream ofthe automated slipstream analyzer. As depicted in this embodiment,automatic splitter comprises a valve on each of the downstreampipelines, in other embodiments this could be different. In oneembodiment as shown in FIG. 1, the automatic splitter is able to directthe refined petroleum product into at least three different downstreampipelines 16, 18 and 20.

As depicted in FIG. 1, the automated slipstream analyzer is placed inthe center of the upstream pipeline. It is understood that in differentembodiments the automated slipstream analyzer can be placed anywherewithin the upstream pipeline capable of collecting a sample of therefined petroleum product.

In another embodiment, the automated slipstream analyzer operates as asample loop adjacent to the upstream pipeline. As shown in FIG. 2,upstream pipeline 50 has an automated slipstream analyzer 52 connectedto the pipeline. The automated slipstream analyzer has an inlet 54capable of collecting a sample and a return 58 capable of returning thesample of refined petroleum product flowing through the upstreampipeline. The inlet can be regulated to be a continuous flow orintermittent based on user needs. The automated slipstream analyzer cananalyze the sample collected from the inlet and generate data from thissample. The data generated form the automated slipstream analyzer can betransferred wirelessly 60 or by a wired connection 62 to an automaticsplitter 64 located downstream of the automated slipstream analyzer. Asdepicted in this embodiment, automatic splitter comprises a valve oneach of the downstream pipelines, in other embodiments this could bedifferent. In one embodiment as shown in FIG. 2, the automatic splitteris able to direct the refined petroleum product into at least threedifferent downstream pipelines 66, 68 and 70.

In one embodiment, a method is taught as shown in FIG. 3. The methodbegins by flowing a product stream through an upstream pipelinecomprising a first product stream 101. The product stream is thencontinuously or periodically analyzed with an automated analyzer toproduce data 103. The first product stream downstream is then directeddownstream of the automated analyzer to a downstream first productstream pipeline 105. The method then changes the product stream flowingthrough the upstream pipeline from the first product stream to a secondproduct stream without purging the first product stream from theupstream pipeline 107, thereby creating a transmix product stream withinthe upstream pipeline wherein the transmix product stream comprises amixture of the first product stream and the second product stream. Thedata from the automated analyzer is then analyzed with an automaticsplitter, wherein the product stream flowing through the upstreampipeline no longer matches the chemical characteristics of the firstproduct stream 109. The automatic splitter then directs the transmixproduct stream downstream of the automatic splitter to a downstreamtransmix pipeline 111. As the data from the automated analyzer is stillanalyzed by the automatic splitter the product stream flowing throughthe upstream pipeline matches the chemical characteristics of the secondproduct stream 113. The automatic splitter then directs the secondproduct stream downstream of the automatic splitter to a downstreamsecond product stream pipeline 115.

In another embodiment, a pipeline interchange is shown in FIG. 4. Inthis embodiment, a first product 201 flows through a first pipeline 203and a second product 205 flows through a second pipeline 207. A pipelineinterchange 209 is connected downstream to both the first pipeline andthe second pipeline, wherein the pipeline interchange blends the firstproduct flowing through the first pipeline with the second productflowing through the second pipeline. A third pipeline 211 connecteddownstream to the pipeline interchange, wherein the third pipeline flowsa blended product 213 created from the blending of the first product andthe second product in the pipeline interchange. An automated analyzer215 is situated downstream of the pipeline interchange capable ofphysically or chemically analyzing the blended product in the thirdpipeline and generating blended data 217. In this embodiment, a dataanalyzer 219 is capable of receiving and interpreting the blended dataand communicate 221 a and 221 b adjustments to values 223 and 225situated in the first pipeline and the second pipeline. These valuesensure that the flow of the first product and the second product are inthe proper ratio to achieve desired physical or chemical characteristicsin the blended product.

In yet another embodiment, a pipeline interchange is shown in FIG. 5. Inthis embodiment, a first product 301 flows through a first pipeline 303and a second product 305 flows through a second pipeline 307. In thisembodiment, an optional first product automated analyzer 309 is situatednear the first pipeline to physically or chemically analyze the firstproduct and generate first product data 311. Additionally, an optionalsecond product automated analyzer 313 is situated near the secondpipeline to physically or chemically analyze the second product andgenerate second product data 315. A pipeline interchange 317 isconnected downstream to both the first pipeline and the second pipeline,wherein the pipeline interchange blends the first product flowingthrough the first pipeline with the second product flowing through thesecond pipeline. A third pipeline 319 connected downstream to thepipeline interchange, wherein the third pipeline flows a blended product321 created from the blending of the first product and the secondproduct in the pipeline interchange. An automated analyzer 323 issituated downstream of the pipeline interchange capable of physically orchemically analyzing the blended product in the third pipeline andgenerating blended data 325. In this embodiment, a data analyzer 327 iscapable of receiving and interpreting the blended data, first productdata, and second product data and communicate 329 a and 329 badjustments to values 331 and 334 situated in the first pipeline and thesecond pipeline. These values ensure that the flow of the first productand the second product are in the proper ratio to achieve desiredphysical or chemical characteristics in the blended product.

In one embodiment, a pipeline interchange is shown in FIG. 6. In thisembodiment, a first product 401 flows through a first pipeline 403 and asecond product 405 flows through a second pipeline 407. In thisembodiment, a first product automated analyzer 409 is situated near thefirst pipeline to physically or chemically analyze the first product andgenerate first product data 411. Additionally, a second productautomated analyzer 413 is situated near the second pipeline tophysically or chemically analyze the second product and generate secondproduct data 415. A pipeline interchange 417 is connected downstream toboth the first pipeline and the second pipeline, wherein the pipelineinterchange blends the first product flowing through the first pipelinewith the second product flowing through the second pipeline. A thirdpipeline 419 connected downstream to the pipeline interchange, whereinthe third pipeline flows a blended product 421 created from the blendingof the first product and the second product in the pipeline interchange.In this embodiment, a data analyzer 427 is capable of receiving andinterpreting the blended data, first product data, and second productdata and communicate 429 a and 429 b adjustments to values 431 and 434situated in the first pipeline and the second pipeline. These valuesensure that the flow of the first product and the second product are inthe proper ratio to achieve desired physical or chemical characteristicsin the blended product.

In another embodiment, a pipeline interchange is shown in FIG. 7. Inthis embodiment, a first product 501 flows through a first pipeline 503and a second product 505 flows through a second pipeline 507. In thisembodiment, a first product automated analyzer 409 is situated near thefirst pipeline to physically or chemically analyze the first product andgenerate first product data 511. Additionally, a second productautomated analyzer 513 is situated near the second pipeline tophysically or chemically analyze the second product and generate secondproduct data 515. A pipeline interchange 517 is connected downstream toboth the first pipeline and the second pipeline, wherein the pipelineinterchange blends the first product flowing through the first pipelinewith the second product flowing through the second pipeline. A thirdpipeline 519 connected downstream to the pipeline interchange, whereinthe third pipeline flows a blended product 521 created from the blendingof the first product and the second product in the pipeline interchange.An optional automated analyzer 523 is situated downstream of thepipeline interchange capable of physically or chemically analyzing theblended product in the third pipeline and generating blended data 525.In this embodiment, a data analyzer 527 is capable of receiving andinterpreting the blended data, first product data, and second productdata and communicate 529 a and 529 b adjustments to values 531 and 534situated in the first pipeline and the second pipeline. These valuesensure that the flow of the first product and the second product are inthe proper ratio to achieve desired physical or chemical characteristicsin the blended product.

In alternative scenarios, it is also possible that the data analyzercommunicates adjustments to a pipeline interchange, wherein the pipelineinterchange has valves within it capable of controlling the flow of thefirst product and the second product to produce a blended product.

In one process embodiment, as shown in FIG. 8, the process begins byflowing a first product through a first pipeline 601. The process thencan optionally analyze the first product in the first pipeline with afirst product automated analyzer that is capable of physically orchemically analyzing the first product in the first pipeline andgenerating a first product data 603. The process can then flow a secondproduct through a second pipeline 605. The process can then optionallyanalyze the second product in the second pipeline with a second productautomated analyzer that is capable of physically or chemically analyzingthe second product in the second pipeline and generating a secondproduct data 607. A blended product can then be produced by mixing boththe first product and the second product within a pipeline interchangewhich is connected downstream to both the first pipeline and the secondpipeline 609. The blended product can the flow from the pipelineinterchange to a third pipeline that is connected downstream of pipelineinterchange 611. The blended product is then analyzed in the thirdpipeline with an automated analyzer that is capable of physically orchemically analyzing the blended product in the third pipeline andgenerating blended data 613. The first product data, the second productdata, and the blended data are then interpreted in a data analyzer bycomparing the physical or chemical characteristics of the blended datato an optimal blended data, the physical or chemical characteristics ofthe first data to an optimal first data, the physical or chemicalcharacteristics of the second data to an optimal second data 615. Thedata analyzer can also determine the adjustments in the flow of thefirst product and the flow of the second product to achieve optimalfirst data, optimal second data, and optimal blended data 617. Theadjustments can then be communicated in the flow of the first product inthe first pipeline and the flow of the second product in the secondpipeline from the data analyzer 619.

In yet another process embodiment, as shown in FIG. 9, the processbegins by flowing a first product through a first pipeline 701. Theprocess then can analyze the first product in the first pipeline with afirst product automated analyzer that is capable of physically orchemically analyzing the first product in the first pipeline andgenerating a first product data 703. The process can then flow a secondproduct through a second pipeline 705. The process can then analyze thesecond product in the second pipeline with a second product automatedanalyzer that is capable of physically or chemically analyzing thesecond product in the second pipeline and generating a second productdata 707. A blended product can then be produced by mixing both thefirst product and the second product within a pipeline interchange whichis connected downstream to both the first pipeline and the secondpipeline 709. The blended product can the flow from the pipelineinterchange to a third pipeline that is connected downstream of pipelineinterchange 711. The blended product is then be optionally analyzed inthe third pipeline with an automated analyzer that is capable ofphysically or chemically analyzing the blended product in the thirdpipeline and generating blended data 713. The first product data, thesecond product data, and the blended data are then interpreted in a dataanalyzer by comparing the physical or chemical characteristics of theblended data to an optimal blended data, the physical or chemicalcharacteristics of the first data to an optimal first data, the physicalor chemical characteristics of the second data to an optimal second data715. The data analyzer can also determine the adjustments in the flow ofthe first product and the flow of the second product to achieve optimalfirst data, optimal second data, and optimal blended data 717. Theadjustments can then be communicated in the flow of the first product inthe first pipeline and the flow of the second product in the secondpipeline from the data analyzer 719.

Certain embodiments of the inventive system and method comprise acomputing device configured to receive data from the automated analyzer.In general, the computing device serves as an interface that receivesand processes the spectral data received from the automated analyzer,then sends a signal/command to the automatic splitter to control itsoperation. In some embodiments, the computing device may be physicallycombined with, or immediately adjacent to, the either automated analyzeror the automatic splitter. However, there is no specific requirement forsuch physical integration and the computing device can optionally belocated at any physical location within the system that allows it to beconfigured to receive data from the automated analyzer and outputsignals that control operation of the automatic splitter.

The computing device comprises a microprocessor with memory thatcontains programming. The programming is configured to provideinstructions to the microprocessor to mathematically transform thespectral data received by the computing device to produce waveletcoefficients data according to wavelet theory by applying a motherwavelet consisting of a member of the Symlet family of mother wavelets(at the third level of decomposition or greater). The programming isadditionally configured to provide instructions to the microprocessorcomprising a trained genetic algorithm that can classify the waveletscoefficients data (representing a given optical or NMR spectrum) intoone of two or more groups based upon one or more spectral featuresrecognized by the trained genetic algorithm within the waveletscoefficients data. In the present inventive systems and methods, the twoor more groups may include any refinery product or intermediate streamthat may be transported by pipeline as well as mixtures of theseproducts or intermediate streams. In certain embodiments, such mixturesmay be classified as “intermix/transmix” by the trained geneticalgorithm.

In certain embodiments, the data generated by the automated analyzer isutilized to predict the fouling propensity of a given sample of crudeoil (or any liquid or gaseous product that can be derived from a crudeoil through commercial refinery processes) requires crude assayinformation that can only be obtained by analytical assays that take sixhours or more to complete. This greatly hampers the rapid identificationof crude oil samples that are capable of fouling process equipment in acommercial refinery setting, leading to potentially increased operatingcosts.

To demonstrate certain embodiments, we developed processes that couldrapidly identify the fouling propensity of a given crude oil sample bydeveloping a model that is based upon near-infrared (NIR) spectral data,nuclear magnetic resonance (NMR) spectra, or both. The inventiveprocesses and systems disclosed herein successfully distinguish aspecific property of a crude oil sample (i.e., the fouling propensity)based solely upon analysis of specific features obtained from thisspectral data. This represents a significant advance in rapidlyidentifying favorable crudes for use in a commercial petroleum refinerysetting.

Certain embodiments of the inventive process utilize spectral dataobtained from at least one of near infrared spectroscopy (NIR) andnuclear magnetic resonance spectroscopy (NMR) because these techniquesare capable of being used to capture “chemical fingerprints” of crudeoil samples that can be correlated with the fouling tendencies of eachsample. More specifically, NIR spectroscopy provides excited vibrationaldata while NMR spectroscopy provides data on magnetic field inducedmolecular chemical shifts that are indicative of the overall molecularcomposition of each crude oil sample. When utilized together, these twotypes of data may help to more easily identify informative spectraldifferences between fouling and non-fouling crude oil samples. However,while this distinguishing information is buried within the spectra, ithas been impossible to identify these differences solely viaconventional attempts to interpret the spectral data. Although NIR andNMR have the unique advantages of capturing significant identifyinginformation about a crude oil sample (when compared to data obtained byother analytical assays), the complexity and subtlety of these spectralsignals has been an obstacle to effectively interpreting differencesbetween crude oil samples.

In addition, certain embodiments of the present inventive process arethe first to successfully combine NIR spectral data and NMR spectraldata to train a genetic algorithm to classify samples of crude oil viathe judicious application of wavelet theory to transform the spectraldata to wavelet coefficients data that is then utilized to train agenetic algorithm. The selection of an unexpectedly advantageous motherwavelet and concatenation of very subtle but informative selectedspectral features enabled the success of the present inventivemethodology.

The inventive process and system in part comprises mathematicaltransformation of the spectral data to wavelets to enhance subtle butinformative features in the data. According to wavelet theory, adiscrete signal such as can be decomposed into “approximation” and“detail” components. Wavelet packet transform (WPT) was applied tode-noise and de-convolute spectra of crude oil samples by decomposingeach spectrum (comprising spectral data) into wavelet coefficients thatrepresent the constituent frequencies of that spectrum.

Wavelets offer a different approach to removal of noise frommultivariate data than other techniques such as Savitzky-Golay filteringor the fast Fourier transform. Wavelets can often enhance subtle butsignificant spectral features to increase the general discriminationpower of the modeling approach. Using wavelets, a new set of basisvectors is developed in a new pattern space that takes advantage of thelocal characteristics of the data. These new basis vectors are capableof better conveying the information present in the data than axes thatare defined by the original measurement variables.

In certain embodiments of the inventive process, spectral signals were“decomposed” by passing each spectrum through low-pass and high-passscaling filters to produce a low-frequency “detail” coefficient datasetand a high-frequency “approximation” coefficient dataset. Theapproximation coefficients correspond to the “low-frequency signal” datain the spectra, while the detail coefficients usually correspond to the“noisy signal” portion of the data. The process of decomposition wascontinued with different scales of the wavelet filter pair in astep-by-step fashion to separate the noisy components from the signaluntil the necessary level of signal decomposition was achieved. We havefound that wavelet coefficients are especially important and preferred(versus raw spectral data) in modeling fouling propensity because thenature of the basis vectors used to characterize the data are conduciveto a variety of approaches for improving the quality of the input datafor training. In particular, we unexpectedly found that decomposition ofthe data using the Symlet6 mother wavelet unexpectedly enabled a geneticalgorithm to distinguish spectral features in the resulting waveletcoefficients data that enabled classification of crude oil samples intotwo groups based upon their fouling propensity.

As mentioned, certain embodiments comprise first obtaining spectralinformation for a given sample comprising crude oil. NIR spectral data,NMR spectral data, or both are obtained by the automated analyzer. Thepresence of numerous signals in the NMR spectrum is beneficial becausethose signals provide a type of fingerprint of the crude oil sample thatprovides spectral features that allow discrimination between oil samplescapable of fouling versus those that are not. FIG. 10 depicts a typicalNMR spectrum of a sample comprising crude oil, which includes a largenumber of signals attributed to diverse hydrocarbon types and complexmolecular structures. The assignments of chemical shift regions to thecorresponding molecular structural types are shown in Table 1 (below).

TABLE 1 Assignments of 1H NMR chemical shifts. Chemical Shift Region(ppm) Assignments 0.2-1.0 aliphatic CH₃ 1.0-1.4 aliphatic CH₂ 1.4-2.0naphthenics or CH in isoparaffins 2.0-2.4 CH₃ alpha to aromatics 2.4-3.5CH₂ or CH alpha to aromatics 3.5-4.5 bridged CH₂ in fluorene types4.5-6.2 olefins or diolefins 6.2-7.4 single-ring aromatics 7.4-8.3diaromatics  8.3-10.0 3-ring polyaromatics and above

The region spanning 0.2-1.0 ppm corresponds to aliphatic CH₃, region1.0-1.4 ppm is assigned to aliphatic CH₂, region 1.4-2.0 ppm is assignedto naphthenics or CH in isoparaffins, region 2.0-2.4 ppm is attributedto CH₃ alpha to aromatics, region 2.4-3.5 ppm corresponds to CH₂ or CHalpha to aromatics, region 3.5-4.5 ppm is assigned to bridged CH₂ influorenes, region 4.5-6.2 ppm is due to olefins or diolefins, region6.2-7.4 ppm is attributed to single-ring aromatics, region 7.4-8.3 ppmcorresponds to diaromatics, and region 8.3-10.0 ppm is assigned topolyaromatics with three rings and above.

Certain embodiments may comprise acquiring ¹³C NMR data rather than ¹HNMR data. While the specifics may differ with regards to chemicalshifts, the general concept is identical to the utilization of ¹H NMRdata in conjunction with the present inventive processes. One havingaverage skill in the area of NMR spectroscopy would be familiar with theimplementation of ¹³C NMR in place of ¹H NMR data, and thus, there is noneed to disclose this in greater detail herein.

Certain embodiments comprise obtaining near-infrared spectral data for asample comprising crude oil. Near-infrared spectral data is typicallyobtained for wavelengths in the range from 3000 to 6000 cm⁻¹. Each NIRspectrum is represented by a finite number of wavelet coefficients thattypically varies from 50-5000 using a member of the Symlet motherwavelet family to perform multiple rounds of data decomposition todenoise and deconvolute the spectra. In certain embodiments, the NIRspectrum may be represented by 300-3000 wavelet coefficients, (e.g.,1333). It is important to note that the selection of alternative motherwavelets other than the Symlet for decomposition of the NIR spectraldata results in a profoundly decreased ability for a pattern recognitiongenetic algorithm to classify crude oil samples according to foulingpropensity.

Wavelet packet transform is applied to de-noise and de-convolute spectraof crude oil samples by decomposing each spectrum into coefficients(wavelet coefficients) that represent the sample's constituentfrequencies. According to wavelet theory, a discrete signal such as anNIR spectrum can be decomposed into “approximation” and “detail”components. Wavelets will often enhance subtle but significant spectralfeatures to increase the general discrimination power of the modelingapproach.

Wavelets offer a different approach to removing noise from multivariatedata than Savitzky-Golay filtering or the fast Fourier transform. Usingwavelets, a new set of basis vectors are developed that take advantageof the local characteristics of the data, and these vectors convey theinformation present in the data better than axes defined by the originalmeasurement variables (wavelength). Wavelet coefficients provide thecoordinates of the samples in this new pattern space. The mother waveletselected to develop the new basis set is the one that best matches theattributes of the data. This gets around the problem that occurs when aninterfering source of variation in the data is correlated to informationabout the class membership of the samples (for example, fouling and/ornon-fouling) as a result of the design of the study or because ofaccidental correlations between signal and noise.

Using wavelets, spectral data is decomposed by passing through twoscaling filters: a high-pass filter and a low-pass filter (FIG. 11). Thelow-pass filter allows only the low-frequency component of the signal tobe measured as a set of wavelet coefficients, which is called the“approximation.” The high-pass filter measures the high-frequencycoefficient set, which is called the “detail.” The detail coefficientsusually correspond to the noisy part of the data. This process ofdecomposition is continued with different scales of the wavelet filterpair in a step-by-step fashion to separate the noisy components from thesignal until the necessary level of signal decomposition has beenachieved. FIG. 12 demonstrates an exemplary result utilizing thistechnique on NIR spectral data (displayed in transmittance mode)obtained from a sample comprising crude oil. FIG. 12 displays theresults of wavelet decomposition of the spectral data performed up tothe sixth level for the approximation (a₁-a₆) and detail (d₁-d₆)components, while “s” indicates the original NIR spectrum.

Wavelet coefficients are especially important and are preferred to rawspectral data for modeling fouling propensity because the nature of thebasis vectors used to characterize the data is conducive to a variety ofapproaches for improving the quality of the data that is used for modeltraining. The wavelet coefficients obtained from each spectrum areorganized as a data vector, and each coefficient is auto-scaled. Amother wavelet is selected to develop the new basis set, andhistorically, certain classes of mother wavelet have been commonlyutilized that are considered to be the most effective at extractingdistinguishing class information from spectral data. Selecting a motherwavelet to use as a “reference point” helps solve the problem thatoccurs when an interfering source of variation in the data is correlatedto information about the class membership of the samples (e.g. foulingand/or non-fouling) as a result of the design of the study or because ofaccidental correlations between signal and noise.

During development of certain embodiments of the process it wasunexpectedly discovered that the Symlet family of mother wavelets wasparticularly well-suited to deconvolute and denoise both NIR and NMRspectral data. In fact, applying the Symlet mother wavelet dramaticallyincreased the power of the inventive process to discriminate betweenfouling and non-fouling crude oil samples. Symlet wavelets are a familyof mother wavelets that are modified versions of Daubechies waveletsthat are characterized by increased symmetry. Daubechies wavelets are afamily of orthogonal wavelets defining a discrete wavelet transform andare characterized by a maximal number of vanishing moments for somegiven support. With each wavelet type of this class, there is a scalingfunction (called the father wavelet) which generates an orthogonalmultiresolution analysis Applying the Symlet mother wavelet to analysisand classification of the NIR and NMR spectral data is highlyunconventional because conventional practice is to perform mathematicaldeconvolution of spectral data that is characterized by broad-bandsignals (e.g., near-infrared spectra) using the “Haar” family of motherwavelets. Conventional wisdom asserts that the Symlet family of motherwavelets should only be applied to decomposition of spectral signalsthat are characterized by numerous sharp peaks (such as mid-infraredspectral data, or chromatographic data) and that the “Haar” family ofmother wavelets is far better-suited for decomposition of NIR or NMRspectral data, which typically do not comprise numerous sharp peaks.

Certain embodiments utilize the Symlet4 mother wavelet, while certainalternative embodiments utilize the Symlet6 mother wavelet. Theinventive process may apply a Symlet mother wavelet at the third orgreater level of decomposition; alternatively, the fourth level ofdecomposition or greater; alternatively, the fifth level ofdecomposition. The choice of the Symlet mother wavelet at the third(alternatively, fourth) level of decomposition was found to unexpectedlyand fortuitously enhance the rather subtle but informative spectralfeatures in the spectra. This resulted in an improved power of theinventive process to discriminate between fouling and non-fouling crudeoil samples.

Certain embodiments of the inventive present process generally comprisetraining a genetic algorithm or utilizing support vector machines todistinguish between wavelet coefficients data obtained for samplescomprising crude oil that are either capable of fouling refinery processequipment versus those that are not. A genetic algorithm is a searchheuristic that is inspired by Charles Darwin's theory of naturalevolution. This algorithm reflects the process of natural selectionwhere the fittest individuals are selected for reproduction in order toproduce offspring of the next generation. The process of naturalselection starts with the selection of fittest individuals from apopulation. They produce offspring which inherit the characteristics ofthe parents and will be added to the next generation. If parents havebetter fitness, their offspring will be better than parents and have abetter chance at surviving. This process is iterative and eventuallyresults in a generation with the fittest individuals identified. Thespecifics of training and applying genetic algorithms is familiar tothose having experience in the field of data analysis, and thus a moredetailed explanation is not provided here.

In certain embodiments of the present inventive process, a geneticalgorithm for pattern-recognition analysis is trained to identifydistinguishing features within wavelet coefficients data derived fromaliquots comprising crude oil, thereby allowing the trained geneticalgorithm to classify crude oil samples into two groups depending ontheir characteristic capacity to foul refinery equipment and processes,or not. In general, a non-fouling crude petroleum sample ischaracterized by a fouling thermal resistance of less than 0.002hr-ft²-° F./British Thermal Unit (BTU), while a fouling crude petroleumsample is characterized by a fouling thermal resistance of at least0.002 hr-ft²-° F./BTU Even in challenging trials, the present inventivemethod correctly classified a variety of crude oil samples as eitherfouling or non-fouling via the identification of selected discriminatingfeatures that were developed from wavelet coefficients and identified bya pattern-recognition genetic algorithm.

In certain embodiments, the genetic algorithm is trained using spectraldata obtained from five or more aliquots of crude oil having varyingfouling thermal resistance characteristics and that are preferably ofdistinct geologic origin. Certainly, a larger number of distinctaliquots of crude oil is preferred and will result in a trained geneticalgorithm that can better discriminate between fouling and non-foulingsamples. In certain embodiments, training the genetic algorithm todistinguish between fouling and non-fouling crude petroleum samples mayutilize the spectral data obtained from at least 20, at least 35, or atleast 50 distinct aliquots of crude oil.

The pattern-recognition genetic algorithm may utilize both supervisedlearning and unsupervised learning to identify the wavelet coefficientsdata that corresponds to features (from NIR data) and/or chemical shifts(from NMR data) that facilitate the ability of the genetic algorithm toclassify each crude oil sample as either fouling or non-fouling. Inembodiments that comprise supervised learning, manual curation toexclude certain data features is performed based upon the probabilitythat such features may have resulted from areas of the spectral datawith a low signal to noise ratio. The result of such manual curation isa subset of features (often the two or three largest principalcomponents of the data) that is utilized by the trained geneticalgorithm to classify each sample comprising crude oil.Pattern-recognition by the genetic algorithm of spectral featuredifferences representing the principal components in crude oil samplesmaximizes the variance between groups (i.e., fouling and non-foulingsamples), which also maximizes the percentage of data utilized by thepattern recognition genetic algorithm to classify each sample that isderived from spectral differences between the groups. Aprincipal-component plot that shows separation of the samples into twogroups can be generated using only a curated subset of spectral featuresthat provide the most information about the differences between thecrude oil samples, simplifying classification along fouling lines. Thisfitness criterion dramatically reduces the size of the search spacebecause it limits the classification search to a small number ofspectral features within the wavelet coefficients data that are capableof distinguishing unknown samples comprising crude oil into one of thetwo classes.

Further, as the pattern-recognition genetic algorithm trains, it focuseson those samples that are difficult to classify by boosting the relativeimportance of distinguishing spectral features associated with thosesamples. Over time, the genetic algorithm learns in a manner similar tohow a neural network learns. The pattern-recognition genetic algorithmintegrates aspects of artificial intelligence and evolutionarycomputations to yield the trained genetic algorithm of the presentinventive processes and systems.

Certain embodiments utilize support vector machines rather than atrained genetic algorithm to classify crude oil samples as eitherfouling or non-fouling. Support vector machines (SVM) and neuralnetworks are examples of non-parametric discriminants that operate byattempting to divide a data space into distinct regions. For a binaryclassifier, the data space is divided into two regions. Samples thatshare a common property will be found on one side of the decisionsurface, while those samples classified in the second category will befound on the other side. The present process efficiently divides thedata space into two regions: crude oil samples that are characterized aseither “fouling” or “non-fouling”.

SVM can be used to address classification and regression problems. Inclassification context, they generate linear boundaries between objectgroups in a transformed space of the x-variables, which are usually ofmuch higher dimension than the original x-space. The idea of thetransformed higher dimensional space is to make groups linearlyseparable. These class boundaries are constructed in order to maximizethe margin between the groups.

SVM identifies decision surfaces or hyperplanes that establish thewidest margin to discriminate between samples belonging to differentclasses in a data set. An example hyperplane is shown in FIG. 13, withsamples representing one class (circles) on one side of the dividinghyperplane and samples representing a second class on the opposite sideof the separating hyperplane. The algorithm uses only some samples inthe data set, which are known as support vectors. The input data aremapped from the original measurement space to a higher-dimensional spaceusing kernel functions to simplify the classification problem. There area variety of kernel functions, including linear kernels for linearhyperplanes as well as polynomial, Gaussian, and sigmoidal kernels fornonlinear decision surfaces. Kernel functions simplify theclassification problem by allowing direct computation of the dot productof the weight vector, which defines the distance between the hyperplaneand each support vector in the original measurement space. For alinearly separable data set, there will be a large number of linearhyperplanes that can be developed to separate samples into theirrespective classes. The hyperplane best able to generalize the data(i.e., accurately classify both the training and test sets) is the onewith the widest margin. For data sets that are not linearly separable, anonlinear decision surface will be used (e.g. separating fouling fromnon-fouling crude oil samples). The kernel function that yields the bestclassification of the samples is selected for discriminant development.When a classification rule is being developed for a non-separable dataset, the optimization problem is reformulated to allow for samples, butas few as possible, to be present in the margin. SVM have some uniqueadvantages. They have good generalization ability because theoptimization problem is less prone to over-fitting when the appropriatekernel functions are used. This is because there are fewer modelparameters to compute from the data. In addition, SVM are easier totrain when compared with conventional methods.

An embodiment of the inventive process and system that trains a GA andthen utilizes the trained GA is illustrated by the flow diagram of FIG.14. In general terms, the embodiment comprises training a geneticalgorithm to recognize subtle collective differences within dataobtained from two groups, a first group comprising aliquots ofnon-fouling crude oil that are each characterized by a fouling thermalresistance of less than 0.002 hr-ft²-° F./BTU and a second groupcomprising aliquots of crude oil that are each characterized by afouling thermal resistance of at least 0.002 hr-ft²-° F./BTU and arecapable of causing fouling in petroleum refinery processes andequipment.

The training wavelet coefficients data obtained from aliquots of thefirst group are collectively compared to the training waveletcoefficients data obtained from aliquots of the second group by thegenetic algorithm, wherein the genetic algorithm performs an iterativeprocess that eventually distinguishes spectral features that differbetween the first group (viewed collectively) and the second group,thereby producing a trained genetic algorithm. The trained geneticalgorithm is then capable of quickly classifying a sample of a crude oilfeed stock (having an unknown fouling thermal resistance) as a member ofthe first or the second group.

In the embodiment shown in FIG. 14, multiple aliquots comprising crudepetroleum that are collectively referred to as the first group 802 andmultiple aliquots comprising crude petroleum that are collectivelyreferred to as the second group 803 are analyzed by a spectral method805 comprising at least one of NIR and NMR to produce spectral data 810,where the spectral data obtained for each aliquot by spectral method 805comprises multiple distinct digitized data points. Each of the firstgroup 802 and the second group 803, respectively, comprise at least fivealiquots, where each aliquot is preferably of distinct geologic originfrom others within the group and in the case of the second group, eachaliquot is characterized by a different fouling thermal resistance of atleast 0.002 hr-ft²-° F./BTU.

The spectral data 810 for each aliquot is transformed to trainingwavelet coefficients data 720 according to wavelet theory by processingthe data using a mother wavelet 815 that comprises a member of theSymlet family of mother wavelets. In certain embodiments, a member ofthe Symlet mother wavelet family that is selected from the Symlet4 andSymlet6 mother wavelets. The mother wavelet 815 is utilized to decomposethe spectral data 810 to the third level of decomposition or greater toproduce training wavelet coefficients data 820. Commercially availablecomputer software (for example, but not limited to, MATLAB®) may beemployed to facilitate the iterative decomposition process, but suchsoftware is not essential in order to practice the inventive process asdescribed herein.

The embodiment trains a genetic algorithm, which comprises presenting anuntrained genetic algorithm 825 that designed to perform data patternrecognition with the training wavelet coefficients data 820 obtainedfrom each of the multiple aliquots comprising the first group 802 andthe second group 803, respectively. While training, the untrainedgenetic algorithm 825 recognizes subtle patterns, or spectral featuresthat are located within the training wavelet coefficients data 820 toproduce a trained genetic algorithm 830 that utilizes a the potentialdifferentiating data features to classify a given sample comprisingcrude oil as either non-fouling or fouling.

The trained genetic algorithm 830 is operable to recognizedifferentiating data features within sample wavelets coefficients data840 that is derived from the sample NIR and/or NMR spectral data 845 ofan uncharacterized sample 850 comprising crude oil. The trained geneticalgorithm 830 then classifies the uncharacterized sample 850 comprisingcrude oil as a either first group feed stock 860 or a second group feedstock 865 wherein the first group feed stock 860 comprises a non-foulingcrude oil sample characterized by a fouling thermal resistance of lessthan 0.002 hr-ft²-° F./BTU and the second group feed stock 865 comprisesa fouling crude oil test sample characterized by a fouling thermalresistance of at least 0.002 hr-ft²-° F./BTU. The uncharacterized sample850 is analyzed by NIR and/or NMR in a similar (or identical) way as wasdescried for the multiple aliquots comprising the first group 802 andthe second group 803 to acquire the sample NIR and/or NMR spectral data845. The sample NIR and/or NMR spectral data 845 is converted to samplewavelets coefficients data 840 that is then presented to the trainedgenetic algorithm 835. The trained genetic algorithm 835 recognizesdifferentiating data features within the sample wavelets coefficientsdata 840, which enables the trained genetic algorithm 835 to classifythe uncharacterized sample 850 as a member of either the first group 860or the second group 865.

Certain embodiments comprise manually curating potential differentiatingfeatures in the wavelets coefficients data that are identified by thegenetic algorithm. This eliminates potential differentiating featureswith the highest probability of being a false positive (i.e., derivedfrom a region of the spectral data that is characterized by a low signalto noise ratio). A second embodiment of the inventive process and systemthat includes manual curation of the data is illustrated by the flowdiagram of FIG. 15.

The embodiment comprises training a pattern-recognition geneticalgorithm to recognize subtle collective differences within dataobtained from two groups, a first group comprising aliquots ofnon-fouling crude oil that are each characterized by a fouling thermalresistance of less than 0.002 hr-ft²-° F./BTU and a second groupcomprising aliquots of crude oil that are each characterized by afouling thermal resistance of at least 0.002 hr-ft²-° F./BTU andtypically capable of causing fouling in petroleum refinery processes andequipment.

Similar to the first embodiment, the training wavelet coefficients dataobtained from aliquots of the first group are collectively compared tothe training wavelet coefficients data obtained from aliquots of thesecond group by the genetic algorithm, wherein the genetic algorithmperforms an iterative process that eventually distinguishes spectralfeatures that differ between the first group (viewed collectively) andthe second group, thereby producing a trained genetic algorithm. Thetrained genetic algorithm is then capable of quickly classifying asample of a crude oil feed stock (having an unknown fouling thermalresistance) as a member of the first or the second group.

In the embodiment shown in FIG. 15, multiple aliquots comprising crudepetroleum that are collectively referred to as the first group 902 andmultiple aliquots comprising crude petroleum that are collectivelyreferred to as the second group 903 are analyzed by a spectral method905 comprising at least one of NIR and NMR to produce spectral data 910,where the spectral data obtained for each aliquot by spectral method 905comprises multiple distinct digitized data points. Each of the firstgroup 902 and the second group 903, respectively, comprise at least fivealiquots, where each aliquot is preferably of distinct geologic originfrom others within the group and in the case of the second group, eachaliquot is characterized by a different fouling thermal resistance of0.002 hr-ft²-° F./BTU or more.

The spectral data 910 for each aliquot is transformed to trainingwavelet coefficients data 820 according to wavelet theory by processingthe data using a mother wavelet 915 that comprises a member of theSymlet family of mother wavelets. In certain embodiments, a member ofthe Symlet mother wavelet family that is selected from the Symlet4 andSymlet6 mother wavelets. The mother wavelet 915 is utilized to decomposethe spectral data 910 to the third level of decomposition or greater toproduce training wavelet coefficients data 920. Commercially availablecomputer software (for example, but not limited to, MATLAB®) may beemployed to facilitate the iterative decomposition process, but suchsoftware is not essential in order to practice the inventive process asdescribed herein.

The embodiment trains a genetic algorithm, which comprises presenting anuntrained genetic algorithm 925 that designed to perform data patternrecognition with the training wavelet coefficients data 920 obtainedfrom each of the multiple aliquots comprising the first group 902 andthe second group 903, respectively. While training, the untrainedgenetic algorithm 925 recognizes subtle patterns, or spectral featuresthat are located within the training wavelet coefficients data 920 toproduce a trained genetic algorithm intermediate 930. In the embodimentdepicted in FIG. 15, potential differentiating data features that arerecognized by the untrained genetic algorithm 925 to produce the trainedgenetic algorithm intermediate 930 are then subjected to manual curation932 to produce a trained genetic algorithm 935 that utilizes a curatedsubset of the potential differentiating data features to classify agiven sample comprising crude oil as either non-fouling or fouling.Manual curation 932 of potential differentiating data features compriseseliminating from consideration any potential differentiating datafeatures recognized by the untrained trained genetic algorithm 925 thatare deemed by either a process operator or an automated curation processto have a high probability of contributing to an inaccurateclassification. Potential differentiating data features most likely tobe subject to manual curation typically are in a region of the spectraldata where the data is typically characterized by a low signal to noiseratio.

The trained genetic algorithm 935 is characterized by a curated subsetof differentiating data features, which makes the trained geneticalgorithm 935 operable to recognize differentiating data features withinsample wavelets coefficients data 940 that is derived from the NIRand/or NMR spectral data 945 of an uncharacterized sample 950 comprisingcrude oil. The trained genetic algorithm 935 then classifies theuncharacterized sample 950 comprising crude oil as a either first groupfeed stock 960 or a second group feed stock 965 wherein a first groupfeed stock 960 comprises a non-fouling crude oil sample characterized bya fouling thermal resistance of less than 0.002 hr-ft²-° F./BTU and asecond group feed stock 965 comprises a fouling crude oil test samplecharacterized by a fouling thermal resistance of at least 0.002 hr-ft²-°F./BTU.

The uncharacterized sample 950 is analyzed by NIR and/or NMR in asimilar (or identical) way as was descried for the multiple aliquotscomprising the first group 902 and the second group 903 to acquire theNIR and/or NMR spectral data 945. The NIR and/or NMR spectral data 945is converted to sample wavelets coefficients data 940 that is thenpresented to the trained genetic algorithm 935. The trained geneticalgorithm 935 recognizes differentiating data features within the samplewavelets coefficients data 940, which enables the trained geneticalgorithm 935 to classify the uncharacterized sample 950 as a member ofeither the first group 960 or the second group 965.

The following examples of certain embodiments of the invention aregiven. Each example is intended to illustrate a specific embodiment, butthe scope of the invention is not intended to be limited to theembodiments specifically disclosed. Rather, the scope is intended to beas broad as is supported by the complete disclosure and the appendingclaims.

Example 1

As a first step toward training a genetic algorithm to distinguish thosecrude oils having the potential to foul refinery processes, 63 crudeoils samples of distinct geologic origin were obtained and their foulingpropensity was determined in a conventional manner using a conventionalHot Liquid Process Simulator (HPLS), prior to analyzing the samples byeither near-infrared spectroscopy (NIR) or nuclear magnetic resonancespectroscopy (NMR). While an HPLS apparatus may be utilized to measurefouling thermal resistance of crude samples utilized with the presentinventive processes, other conventional laboratory tests may also beutilized to measure fouling thermal resistance.

The general operational layout of a HPLS is diagramed in FIG. 16. TheHPLS pumps a test liquid upward through an annular test section thatcontains a heated rod made of carbon steel. The heated rod is hollow,with an internally mounted control thermocouple, and the rod iselectrically heated to maintain constant temperature. The following testconditions were chosen:

Rod temperature: 698° F. (370° C.)

Rod Metallurgy: Carbon Steel

Liquid feed line and pump temperature: 22° C.

Flow rate: 1 mL/min (no recirculation)

Line pressure: 500 psig

As foulants accumulate on the rod, they insulate the rod and reduce therate of heat transfer to the liquid, thereby causing a decrease overtime of the temperature of the test liquid measured at the outlet of thetest chamber containing the heated rod. The results are expressed asfouling thermal resistance (FTR) per unit of time (set at 1 hr) per sq.ft. where FTR is equal to the inverse of the heat transfer coefficient(or, 1/U). Thus, FTR=1/U=A×ΔT÷Q, where

Q=heater rod power

U=heat transfer coefficient

A=surface area of the heater rod

ΔT=change in temperature of the rod=T_(ROD)−(T_(INLET)+T_(OUTLET))/2

Using the HPLS, FTR values were determined for the 63 different aliquotscomprising crude oil feed stocks of distinct geologic origin andobtained from different regions around the world. The resulting FTRvalues ranged from 0.000 hr-ft²-° F./BTU to 0.00616 hr-ft²-°F./BTU. Acrude oil sample characterized by a FTR value below 0.002 hr-ft²-°F./BTU (as measured by the HPLS) were classified as “non-fouling” (Group1), while a crude oil sample with a fouling thermal resistance that metor exceeded a value of 0.002 hr-ft²-° F./BTU or above was classified as“fouling”. All units of thermal fouling resistance in the presentdisclosure are as measured by the HPLS method, which may not necessarilycoincide with actual fouling thermal resistance.

Example 2

From the 63 samples comprising crude oil that were characterized forfouling characteristics in Example 1, approximately 30 aliquotscomprising crude oil were chosen that were characterized as“non-fouling” (Group 1) according to the criteria described above, andapproximately 30 samples were chosen that were characterized as“non-fouling” (Group 2). The samples were first analyzed by NIR toobtain spectral data comprising more than 1300 discrete digitized datapoints in the range from 4000 to 6000 cm⁻¹. Measurements were carriedout on an ABB Bomem FT-NIR spectrometer equipped with a deuteratedtriglycine sulfate (DTGS) detector. Samples were scanned in a fixed 0.5mm cell with sapphire windows at a temperature of 90° F. (32.2° C.). Thespectral resolution was 4 cm′ and the number of sample scans andbackground scans was 32, respectively.

The Group 1 and Group 2 samples (described above) were also eachanalyzed by ¹H nuclear magnetic resonance spectroscopy (NMR) to obtainNMR spectral data. A representative ¹H NMR spectrum of a samplecomprising crude oil is shown in FIG. 10, and methods for obtaining suchspectral data are both described herein, and conventional in nature.They will therefore not be described in further detail here.

The digitized spectral data obtained by both NIR and NMR was thentransformed by wavelet packet transform according to wavelet theory toproduce wavelet coefficients data. Each spectrum comprisingnear-infrared spectral data was decomposed according to wavelet theoryusing a mother wavelet from the Symlet family of mother wavelets.Decomposition comprised passing the spectral data through two scalingfilters: a high pass filter and a low pass filter. As mentionedpreviously, FIG. 11 demonstrates how the high-pass scaling filterallowed only the high-frequency component of the original spectral datato be converted to an “detail coefficient data set”, while the low-passscaling filter allowed only the low-frequency component of the originalspectral data to be converted to an “approximation coefficient dataset”. Commercially available computer software (for example, but notlimited to MATLAB®) may be employed to assist in this transformation butis not required in order to practice the inventive process as describedherein.

The process of signal decomposition was continued with different scalesof the wavelet filter pair in a step-by-step fashion to separate thenoisy components from the signal until the appropriate level of signaldecomposition was achieved. Applying the Symlet6 mother wavelet, it wasdetermined that the third level of decomposition or greater alloweddiscrimination of signal from noise. In certain embodiments, the fourthlevel of decomposition or greater allowed discrimination of signal fromnoise. A distinct decrease in ability to discriminate signal from noiseoccurred when non-Symlet mother wavelets were utilized.

The decomposed wavelet coefficient data for each of the 63 crude oilsamples were used to train a pattern-recognition genetic algorithm torecognize potential spectral features that might allow the algorithm todistinguish between samples characterized as “fouling” and samplescharacterized as “non-fouling”. Training a pattern-recognition geneticalgorithm comprised presenting an untrained genetic algorithm (designedfor pattern recognition) with the training wavelet coefficients dataobtained from the multiple aliquots representing the first group and thesecond group, respectively. As the genetic algorithm examined the dataand identified potential features in the wavelets coefficients data thatcould assist in differentiating between the two classes, certainidentified potential features were eliminated by manual curation toeliminate identified potential features with the highest probability ofbeing a false positive (i.e., derived from an region of the data that ischaracterized by a low signal to noise ratio). The process of manualcuration can also be thought of as a “search pre-filtering” thatpre-screens data that is used by the final trained genetic algorithm toclassify samples. Manual curation or pre-filtering served to: 1)decrease the total data to be reviewed by the genetic algorithm whenclassifying a sample, and 2) assure that potential features that werethe result of noise in the data were not utilized by the trained geneticalgorithm during classification samples comprising crude oil. Theremaining features that were utilized by the trained genetic algorithmfor classification typically were associated with spectral featuresassociated with the 2-3 most prevalent classes of chemical components inthe sample. One example of classification performed by a trained geneticalgorithm is provided in FIG. 17, which depicts wavelet coefficient datafor each of two principle components (principle Component 1=PC1;Principle Component 2=PC2) in the training aliquots comprising crudeoil, plotted relative to each other. The trained genetic algorithmcorrectly categorized each aliquot as belonging to either thenon-fouling first group (plotted as samples represented by thenumeral 1) or the fouling second group (plotted as samples representedby the numeral 2).

Once the pattern recognition genetic algorithm was trained using thevarious training aliquots the trained genetic algorithm was competent toaccurately classify the relative fouling potential of unknown samplescomprising crude oil.

Example 3

Certain embodiments utilize support vector machines rather than agenetic algorithm to recognize potential differentiating spectralfeatures in wavelet coefficients data and classify samples comprisingcrude oil as either fouling or non-fouling. FIG. 18 graphically depictsthe strong correlation between measured and predicted fouling potentialfor a variety of crude oil samples when using SVM to classify samples.The plots predicted fouling thermal resistance for a given sample (usingsupport vector machines) versus the actual fouling thermal resistance ofthe aliquot measured using the HPLS method described in Example 1. Thehorizontal and vertical lines through the plotted area represent thethreshold fouling thermal resistance that would classify a given sampleas either non-fouling (Group A in figure) or fouling (Group B in thefigure).

Wavelet coefficients data was first obtained from NIR spectral data (asdescribed herein) for various training aliquots comprising eithernon-fouling (Group A) or fouling (Group B) crude oil. The waveletscoefficients data was analyzed by SVM to generate a best fit hyperplanethrough the data (see FIG. 10) that would divide the training aliquotsinto non-fouling (Group A in the figure) and fouling groups (Group B inthe figure), thereby producing a trained SVM. Analyzing of samplescomprising crude oil by the trained SVM allowed most samples comprisingcrude oil to be easily classified according to fouling propensity.

In FIG. 18, each plotted point (small circle) represents the correlationbetween the measured fouling thermal resistance (x-axis) measured byHPLS (as in Example 1) for a given training sample versus the foulingthermal resistance predicted by SVM (y-axis). The good predictiveability of the SVM to accurately classify the training samples isindicated by the observed strong direct correlation for the trainingdata points (small circles). The trained SVM was then utilized toproperly classify four unknown samples comprising crude oil (largeovals), which were all determined to fall within Group A (lower leftquadrant of the graph) corresponding to “non-fouling” samples andconfirmed the predictive ability of the SVM model. Samples in “Group B”would have been classified as “fouling”.

Although the systems and processes described herein have been describedin detail, various changes, substitutions, and alterations can be madewithout departing from the spirit and scope of the invention asdelineated by the following claims. Further, the description, abstractand drawings are not intended to limit the scope of the claims to theembodiments disclosed.

In the present description, the term fouling refers to depositformation, encrustation, scaling, scale formation, slagging, and sludgeformation in a petroleum refinery setting, which has an adverse effecton operations. It is the accumulation of unwanted material within arefinery processing unit or on solid surfaces of the unit that isdetrimental to function. When it does occur during refinery operations,the major effects include (1) loss of heat transfer as indicated bycharge outlet temperature decrease and pressure drop increase, (2)blocked process pipes, (3) under-deposit corrosion and pollution, and(4) localized hot spots in reactors and furnace tubes, all of which leadto production losses and increased maintenance costs. In closing, itshould be noted that the discussion of any reference is not an admissionthat it is prior art to the present invention, especially any referencethat may have a publication date after the priority date of thisapplication. At the same time, each and every claim below is herebyincorporated into this detailed description or specification as anadditional embodiment of the present invention.

Although the systems and processes described herein have been describedin detail, it should be understood that various changes, substitutions,and alterations can be made without departing from the spirit and scopeof the invention as defined by the following claims. Those skilled inthe art may be able to study the preferred embodiments and identifyother ways to practice the invention that are not exactly as describedherein. It is the intent of the inventors that variations andequivalents of the invention are within the scope of the claims whilethe description, abstract and drawings are not to be used to limit thescope of the invention. The invention is specifically intended to be asbroad as the claims below and their equivalents.

The invention claimed is:
 1. A process comprising: flowing a firstproduct through a first pipeline; flowing a second product through asecond pipeline; producing a blended product by mixing both the firstproduct and the second product within a pipeline interchange which isconnected downstream to both the first pipeline and the second pipeline;flowing the blended product from the pipeline interchange to a thirdpipeline that is connected downstream of pipeline interchange; analyzingthe blended product in the third pipeline with an automated analyzerthat is capable of physically or chemically analyzing the blendedproduct in the third pipeline and generating blended data; interpretingthe blended data in a data analyzer by comparing the physical orchemical characteristics of the blended data to an optimal blended dataand determining the adjustments in the flow of the first product and theflow of the second product to achieve optimal blended data from theblended product; and communicating the adjustments in the flow of thefirst product in the first pipeline and the flow of the second productin the second pipeline, wherein the automated analyzer is selected fromthe group consisting of: infrared analyzer, near infrared analyzer,ramen analyzer, acoustic analyzer, UV-visible analyzer, densityanalyzer, NMR analyzer, viscometer analyzer, terahertz spectroscopyanalyzer, conductivity analyzer, pH analyzer, mass spectrometryanalyzer, turbidity analyzer, particle size analyzer, electron lossspectroscopy analyzer, fluorescence analyzer, micro- andnano-electromechanical systems analyzers, chromatography analyzer,electrophoresis analyzer, microwave resonance analyzer, cavity-ringdownanalyzer, cavity-enhanced analyzer, dielectric spectroscopy, surfaceplasmon resonance analyzer, quartz crystal microbalance analyzer,biosensing analyzers, time resolved spectrometers, and micro totalanalytical systems analyzer and combinations thereof, and wherein theanalyzing of the data from the automatic analyzer comprises periodicallytransforming the spectral data to produce transformed spectral data andcorrelating the transformed spectral data with at least one physicaland/or chemical characteristic of the product stream flowing through theupstream pipeline, wherein the correlating indicates a change in theproduct stream from the first stream to a transmix stream and to asecond stream, wherein changes in at least one physical and/or chemicalcharacteristic of the product stream are determined by: converting thespectral data to discrete digitized data points that are thentransformed to produce wavelet coefficients data according to wavelettheory by applying a mother wavelet consisting of a member of the Symletfamily of mother wavelets at the third level of decomposition or greaterto produce transformed wavelet coefficients data; and classifying thestream represented by the transformed wavelet coefficients data aseither the first stream, a transmix stream or a second stream havingdistinct chemical characteristics from the first stream by presentingthe transformed wavelets coefficients data to a trained geneticalgorithm, wherein the trained genetic algorithm performs theclassifying by examining identifying data features that collectivelyidentify a particular stream as a member of the first stream, the secondstream or a transmix stream.
 2. The process of claim 1, wherein the dataanalyzer utilizes chemometrics to determine the modelling relationshipof the blended product to the first product and the second product. 3.The process of claim 2, wherein the modelling relationship isperiodically updated from the blended data.
 4. The process of claim 1,wherein the automated analyzer physically or chemically analyzes theblended product inside the third pipeline without extracting a sample.5. The process of claim 1, wherein the automated analyzer physically orchemically analyzes the blended product by extracting a sample from thethird pipeline.
 6. The process of claim 1, wherein the pipelineinterchange controls the flow of the first pipeline and the secondpipeline to produce the blended product.
 7. The process of claim 1,wherein the flow of the first pipeline is controlled more than 500meters from the pipeline interchange.
 8. The process of claim 1, whereinthe flow of the second pipeline is controlled more than 500 meters fromthe pipeline interchange.
 9. A process comprising: flowing a firstproduct through a first pipeline; optionally analyzing the first productin the first pipeline with a first product automated analyzer that iscapable of physically or chemically analyzing the first product in thefirst pipeline and generating a first product data; flowing a secondproduct through a second pipeline; analyzing the second product in thesecond pipeline with a second product automated analyzer that is capableof physically or chemically analyzing the second product in the secondpipeline and generating a second product data; producing a blendedproduct by mixing both the first product and the second product within apipeline interchange which is connected downstream to both the firstpipeline and the second pipeline; flowing the blended product from thepipeline interchange to a third pipeline that is connected downstream ofpipeline interchange; analyzing the blended product in the thirdpipeline with an automated analyzer that is capable of physically orchemically analyzing the blended product in the third pipeline andgenerating blended data; interpreting the first product data, the secondproduct data, and the blended data in a data analyzer by comparing thephysical or chemical characteristics of the blended data to an optimalblended data, the physical or chemical characteristics of the first datato an optimal first data, the physical or chemical characteristics ofthe second data to an optimal second data and determining theadjustments in the flow of the first product and the flow of the secondproduct to achieve optimal first data, optimal second data, and optimalblended data; and communicating the adjustments in the flow of the firstproduct in the first pipeline and the flow of the second product in thesecond pipeline, wherein the automated analyzer is selected from thegroup consisting of: infrared analyzer, near infrared analyzer, ramenanalyzer, acoustic analyzer, UV-visible analyzer, density analyzer, NMRanalyzer, viscometer analyzer, terahertz spectroscopy analyzer,conductivity analyzer, pH analyzer, mass spectrometry analyzer,turbidity analyzer, particle size analyzer, electron loss spectroscopyanalyzer, fluorescence analyzer, micro- and nano-electromechanicalsystems analyzers, chromatography analyzer, electrophoresis analyzer,microwave resonance analyzer, cavity-ringdown analyzer, cavity-enhancedanalyzer, dielectric spectroscopy, surface plasmon resonance analyzer,quartz crystal microbalance analyzer, biosensing analyzers, timeresolved spectrometers, and micro total analytical systems analyzer andcombinations thereof, and wherein the analyzing of the data from theautomatic analyzer comprises periodically transforming the spectral datato produce transformed spectral data and correlating the transformedspectral data with at least one physical and/or chemical characteristicof the product stream flowing through the upstream pipeline, wherein thecorrelating indicates a change in the product stream from the firststream to a transmix stream and to a second stream, wherein changes inat least one physical and/or chemical characteristic of the productstream are determined by: converting the spectral data to discretedigitized data points that are then transformed to produce waveletcoefficients data according to wavelet theory by applying a motherwavelet consisting of a member of the Symlet family of mother waveletsat the third level of decomposition or greater to produce transformedwavelet coefficients data; and classifying the stream represented by thetransformed wavelet coefficients data as either the first stream, atransmix stream or a second stream having distinct chemicalcharacteristics from the first stream by presenting the transformedwavelets coefficients data to a trained genetic algorithm, wherein thetrained genetic algorithm performs the classifying by examiningidentifying data features that collectively identify a particular streamas a member of the first stream the second stream or a transmix stream.